1. Field of the Invention
This invention pertains to gamut mapping, and in particular, pertains to gamut mapping in spectral space based on an objective function.
2. Related Background Art
There is an increasing trend for color imaging applications to process and utilize spectral data, and/or to perform color processing in a spectral color space. For example, it has become common to characterize color devices such as scanners, cameras, printers and displays, in terms of their spectral responsivity, such as by obtaining a so-called spectral device profile. Once a spectral characterization of a device is obtained, operations such as transforming color data from a source device to a destination device, gamut mapping, etc., can be performed in the full spectral space.
As another example, some modern image capture devices can capture color image data in spectral or extended color spaces. In this regard, the use of spectral data such as spectral reflectance of objects and spectral power distribution of illuminants promises to yield even more advantages in the near future. Ultimately, one goal of spectral color reproduction is to produce color reproductions with remarkably high color fidelity, perhaps even matching the original scene on the spectral level. In the case of reflective objects, for example, a reproduction with a spectral match would have the same color as the original object under any illuminant.
On the other hand, other goals are possible. For example, instead of a spectral match, the goal may be to preserve the color appearance of an object under one illuminant even when it is viewed under a different illuminant. In other words, the goal may be to minimize color inconstancy under different illuminants. In fact, the human visual system has a built-in mechanism to achieve this: the associated phenomenon is known as “discounting the illuminant”. However, when the new illuminant is drastically different from the original illuminant, the human visual system will detect the difference and the result is that the color of object “appears different” under the new illuminant. Notice that a spectral match would not preserve the color appearance under different illuminants, because even the original object would appear different under different illuminants. Preserving the color appearance so that it is resistant to change of illuminant could be an important objective for color critical business where a customer may purchase a commodity in a store under one light source only to find out that it looks significantly different under a different light source, for example, at home.
A spectral color management workflow can have very useful applications in everyday consumer products. For example, such technologies could allow online shoppers to print their own “virtual swatches” of fabrics, as detailed in the paper “Answering Hunt's Web Shopping Challenge: Spectral Color Management for a Virtual Swatch” by Rosen et al., Proceedings of the IS&T/SID 9th Color Imaging Conference, pp. 267-272. Another application of spectral color management in computer aided design/computer aided manufacturing (CAD/CAM) applications allows manufacturers to visualize and demonstrate preliminary design ideas by simulation, before actually building a costly prototype.
While the use of spectral data can provide many advantages, such as reducing metamerism or minimizing color inconstancy, using spectral data presents some challenges in the context of gamut mapping. As in the colorimetric case where different devices have different colorimetric gamuts, in the spectral case, different devices have different spectral gamuts. Spectral color reproduction requires accommodating the difference in spectral gamuts. A standard approach to this problem in the calorimetric case is gamut mapping. Extending conventional, colorimetric gamut mapping algorithms to spectral gamut mapping algorithms however requires overcoming some difficulties. In particular, unlike conventional color spaces such as CIELAB and CIECAM02, there is no appearance attributes such as lightness or chroma to establish mapping direction. A basic question for spectral gamut mapping is how to design algorithms that do not require these appearance attributes.
Another difficulty comes from the high dimensionality of the spectral data. A known approach to this problem is the construction of Interim Connection Spaces (ICSs), which are of lower dimension than the full spectral space. Gamut mapping in ICSs instead of the full spectral space can alleviate some of the computational burden associated with high dimension, but the problem is still formidable without novel algorithms. This is because, although the dimension of the ICS is lower, it is nevertheless higher than three dimensions. Consequently, use of ICS in computationally intensive operations such as certain nonlinear optimization methods used in gamut mapping can be a major bottleneck in a spectral color management computational pipeline.